Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Tuesday 21 July 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 183,045 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… miss… e380000… nhs_glo… 1 gl34fe South West
## [90m 2[39m 111 2020-03-18 fema… miss… e380001… nhs_sou… 1 ne325nn North Eas…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_air… 8 bd57jr North Eas…
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ash… 7 tn254ab South East
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 9 n111np London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 11 s752py North Eas…
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 19 ss143hg East of E…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 6 dn227xf North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bat… 9 ba25rp South West
## [90m# … with 183,035 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 65
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 100
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
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## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 7
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 1
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 8
## 108 2020-06-16 East of England 3
## 109 2020-06-17 East of England 7
## 110 2020-06-18 East of England 4
## 111 2020-06-19 East of England 7
## 112 2020-06-20 East of England 4
## 113 2020-06-21 East of England 3
## 114 2020-06-22 East of England 6
## 115 2020-06-23 East of England 5
## 116 2020-06-24 East of England 4
## 117 2020-06-25 East of England 1
## 118 2020-06-26 East of England 5
## 119 2020-06-27 East of England 6
## 120 2020-06-28 East of England 8
## 121 2020-06-29 East of England 4
## 122 2020-06-30 East of England 5
## 123 2020-07-01 East of England 2
## 124 2020-07-02 East of England 5
## 125 2020-07-03 East of England 0
## 126 2020-07-04 East of England 3
## 127 2020-07-05 East of England 1
## 128 2020-07-06 East of England 2
## 129 2020-07-07 East of England 2
## 130 2020-07-08 East of England 0
## 131 2020-07-09 East of England 8
## 132 2020-07-10 East of England 4
## 133 2020-07-11 East of England 2
## 134 2020-07-12 East of England 1
## 135 2020-07-13 East of England 7
## 136 2020-07-14 East of England 2
## 137 2020-07-15 East of England 0
## 138 2020-07-16 East of England 0
## 139 2020-07-17 East of England 0
## 140 2020-07-18 East of England 0
## 141 2020-07-19 East of England 0
## 142 2020-03-01 London 0
## 143 2020-03-02 London 0
## 144 2020-03-03 London 0
## 145 2020-03-04 London 0
## 146 2020-03-05 London 0
## 147 2020-03-06 London 1
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## 289 2020-03-07 Midlands 0
## 290 2020-03-08 Midlands 2
## 291 2020-03-09 Midlands 1
## 292 2020-03-10 Midlands 0
## 293 2020-03-11 Midlands 2
## 294 2020-03-12 Midlands 6
## 295 2020-03-13 Midlands 5
## 296 2020-03-14 Midlands 4
## 297 2020-03-15 Midlands 5
## 298 2020-03-16 Midlands 11
## 299 2020-03-17 Midlands 8
## 300 2020-03-18 Midlands 13
## 301 2020-03-19 Midlands 8
## 302 2020-03-20 Midlands 28
## 303 2020-03-21 Midlands 13
## 304 2020-03-22 Midlands 31
## 305 2020-03-23 Midlands 33
## 306 2020-03-24 Midlands 41
## 307 2020-03-25 Midlands 48
## 308 2020-03-26 Midlands 64
## 309 2020-03-27 Midlands 72
## 310 2020-03-28 Midlands 89
## 311 2020-03-29 Midlands 92
## 312 2020-03-30 Midlands 90
## 313 2020-03-31 Midlands 123
## 314 2020-04-01 Midlands 140
## 315 2020-04-02 Midlands 142
## 316 2020-04-03 Midlands 124
## 317 2020-04-04 Midlands 151
## 318 2020-04-05 Midlands 164
## 319 2020-04-06 Midlands 140
## 320 2020-04-07 Midlands 123
## 321 2020-04-08 Midlands 186
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## 336 2020-04-23 Midlands 103
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## 339 2020-04-26 Midlands 81
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## 342 2020-04-29 Midlands 53
## 343 2020-04-30 Midlands 56
## 344 2020-05-01 Midlands 64
## 345 2020-05-02 Midlands 51
## 346 2020-05-03 Midlands 52
## 347 2020-05-04 Midlands 61
## 348 2020-05-05 Midlands 59
## 349 2020-05-06 Midlands 59
## 350 2020-05-07 Midlands 48
## 351 2020-05-08 Midlands 34
## 352 2020-05-09 Midlands 37
## 353 2020-05-10 Midlands 42
## 354 2020-05-11 Midlands 33
## 355 2020-05-12 Midlands 45
## 356 2020-05-13 Midlands 40
## 357 2020-05-14 Midlands 38
## 358 2020-05-15 Midlands 40
## 359 2020-05-16 Midlands 34
## 360 2020-05-17 Midlands 31
## 361 2020-05-18 Midlands 36
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## 363 2020-05-20 Midlands 36
## 364 2020-05-21 Midlands 32
## 365 2020-05-22 Midlands 27
## 366 2020-05-23 Midlands 34
## 367 2020-05-24 Midlands 20
## 368 2020-05-25 Midlands 26
## 369 2020-05-26 Midlands 33
## 370 2020-05-27 Midlands 29
## 371 2020-05-28 Midlands 28
## 372 2020-05-29 Midlands 20
## 373 2020-05-30 Midlands 21
## 374 2020-05-31 Midlands 22
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## 376 2020-06-02 Midlands 22
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## 381 2020-06-07 Midlands 17
## 382 2020-06-08 Midlands 16
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## 385 2020-06-11 Midlands 13
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## 420 2020-07-16 Midlands 1
## 421 2020-07-17 Midlands 1
## 422 2020-07-18 Midlands 1
## 423 2020-07-19 Midlands 1
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## 427 2020-03-04 North East and Yorkshire 0
## 428 2020-03-05 North East and Yorkshire 0
## 429 2020-03-06 North East and Yorkshire 0
## 430 2020-03-07 North East and Yorkshire 0
## 431 2020-03-08 North East and Yorkshire 0
## 432 2020-03-09 North East and Yorkshire 0
## 433 2020-03-10 North East and Yorkshire 0
## 434 2020-03-11 North East and Yorkshire 0
## 435 2020-03-12 North East and Yorkshire 0
## 436 2020-03-13 North East and Yorkshire 0
## 437 2020-03-14 North East and Yorkshire 0
## 438 2020-03-15 North East and Yorkshire 2
## 439 2020-03-16 North East and Yorkshire 3
## 440 2020-03-17 North East and Yorkshire 1
## 441 2020-03-18 North East and Yorkshire 2
## 442 2020-03-19 North East and Yorkshire 6
## 443 2020-03-20 North East and Yorkshire 5
## 444 2020-03-21 North East and Yorkshire 6
## 445 2020-03-22 North East and Yorkshire 7
## 446 2020-03-23 North East and Yorkshire 9
## 447 2020-03-24 North East and Yorkshire 8
## 448 2020-03-25 North East and Yorkshire 18
## 449 2020-03-26 North East and Yorkshire 21
## 450 2020-03-27 North East and Yorkshire 28
## 451 2020-03-28 North East and Yorkshire 35
## 452 2020-03-29 North East and Yorkshire 38
## 453 2020-03-30 North East and Yorkshire 64
## 454 2020-03-31 North East and Yorkshire 60
## 455 2020-04-01 North East and Yorkshire 67
## 456 2020-04-02 North East and Yorkshire 75
## 457 2020-04-03 North East and Yorkshire 100
## 458 2020-04-04 North East and Yorkshire 105
## 459 2020-04-05 North East and Yorkshire 92
## 460 2020-04-06 North East and Yorkshire 96
## 461 2020-04-07 North East and Yorkshire 102
## 462 2020-04-08 North East and Yorkshire 107
## 463 2020-04-09 North East and Yorkshire 111
## 464 2020-04-10 North East and Yorkshire 117
## 465 2020-04-11 North East and Yorkshire 98
## 466 2020-04-12 North East and Yorkshire 84
## 467 2020-04-13 North East and Yorkshire 94
## 468 2020-04-14 North East and Yorkshire 107
## 469 2020-04-15 North East and Yorkshire 96
## 470 2020-04-16 North East and Yorkshire 103
## 471 2020-04-17 North East and Yorkshire 88
## 472 2020-04-18 North East and Yorkshire 95
## 473 2020-04-19 North East and Yorkshire 88
## 474 2020-04-20 North East and Yorkshire 100
## 475 2020-04-21 North East and Yorkshire 76
## 476 2020-04-22 North East and Yorkshire 84
## 477 2020-04-23 North East and Yorkshire 63
## 478 2020-04-24 North East and Yorkshire 72
## 479 2020-04-25 North East and Yorkshire 69
## 480 2020-04-26 North East and Yorkshire 65
## 481 2020-04-27 North East and Yorkshire 65
## 482 2020-04-28 North East and Yorkshire 57
## 483 2020-04-29 North East and Yorkshire 69
## 484 2020-04-30 North East and Yorkshire 57
## 485 2020-05-01 North East and Yorkshire 64
## 486 2020-05-02 North East and Yorkshire 48
## 487 2020-05-03 North East and Yorkshire 40
## 488 2020-05-04 North East and Yorkshire 49
## 489 2020-05-05 North East and Yorkshire 40
## 490 2020-05-06 North East and Yorkshire 51
## 491 2020-05-07 North East and Yorkshire 45
## 492 2020-05-08 North East and Yorkshire 42
## 493 2020-05-09 North East and Yorkshire 44
## 494 2020-05-10 North East and Yorkshire 40
## 495 2020-05-11 North East and Yorkshire 29
## 496 2020-05-12 North East and Yorkshire 27
## 497 2020-05-13 North East and Yorkshire 28
## 498 2020-05-14 North East and Yorkshire 31
## 499 2020-05-15 North East and Yorkshire 32
## 500 2020-05-16 North East and Yorkshire 35
## 501 2020-05-17 North East and Yorkshire 26
## 502 2020-05-18 North East and Yorkshire 30
## 503 2020-05-19 North East and Yorkshire 27
## 504 2020-05-20 North East and Yorkshire 22
## 505 2020-05-21 North East and Yorkshire 33
## 506 2020-05-22 North East and Yorkshire 22
## 507 2020-05-23 North East and Yorkshire 18
## 508 2020-05-24 North East and Yorkshire 26
## 509 2020-05-25 North East and Yorkshire 21
## 510 2020-05-26 North East and Yorkshire 21
## 511 2020-05-27 North East and Yorkshire 22
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## 513 2020-05-29 North East and Yorkshire 25
## 514 2020-05-30 North East and Yorkshire 20
## 515 2020-05-31 North East and Yorkshire 20
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## 530 2020-06-15 North East and Yorkshire 9
## 531 2020-06-16 North East and Yorkshire 10
## 532 2020-06-17 North East and Yorkshire 9
## 533 2020-06-18 North East and Yorkshire 11
## 534 2020-06-19 North East and Yorkshire 6
## 535 2020-06-20 North East and Yorkshire 5
## 536 2020-06-21 North East and Yorkshire 4
## 537 2020-06-22 North East and Yorkshire 6
## 538 2020-06-23 North East and Yorkshire 7
## 539 2020-06-24 North East and Yorkshire 10
## 540 2020-06-25 North East and Yorkshire 4
## 541 2020-06-26 North East and Yorkshire 7
## 542 2020-06-27 North East and Yorkshire 3
## 543 2020-06-28 North East and Yorkshire 5
## 544 2020-06-29 North East and Yorkshire 2
## 545 2020-06-30 North East and Yorkshire 5
## 546 2020-07-01 North East and Yorkshire 1
## 547 2020-07-02 North East and Yorkshire 4
## 548 2020-07-03 North East and Yorkshire 3
## 549 2020-07-04 North East and Yorkshire 4
## 550 2020-07-05 North East and Yorkshire 2
## 551 2020-07-06 North East and Yorkshire 2
## 552 2020-07-07 North East and Yorkshire 3
## 553 2020-07-08 North East and Yorkshire 3
## 554 2020-07-09 North East and Yorkshire 0
## 555 2020-07-10 North East and Yorkshire 3
## 556 2020-07-11 North East and Yorkshire 1
## 557 2020-07-12 North East and Yorkshire 4
## 558 2020-07-13 North East and Yorkshire 1
## 559 2020-07-14 North East and Yorkshire 1
## 560 2020-07-15 North East and Yorkshire 2
## 561 2020-07-16 North East and Yorkshire 2
## 562 2020-07-17 North East and Yorkshire 1
## 563 2020-07-18 North East and Yorkshire 2
## 564 2020-07-19 North East and Yorkshire 0
## 565 2020-03-01 North West 0
## 566 2020-03-02 North West 0
## 567 2020-03-03 North West 0
## 568 2020-03-04 North West 0
## 569 2020-03-05 North West 1
## 570 2020-03-06 North West 0
## 571 2020-03-07 North West 0
## 572 2020-03-08 North West 1
## 573 2020-03-09 North West 0
## 574 2020-03-10 North West 0
## 575 2020-03-11 North West 0
## 576 2020-03-12 North West 2
## 577 2020-03-13 North West 3
## 578 2020-03-14 North West 1
## 579 2020-03-15 North West 4
## 580 2020-03-16 North West 2
## 581 2020-03-17 North West 4
## 582 2020-03-18 North West 6
## 583 2020-03-19 North West 7
## 584 2020-03-20 North West 10
## 585 2020-03-21 North West 11
## 586 2020-03-22 North West 13
## 587 2020-03-23 North West 15
## 588 2020-03-24 North West 21
## 589 2020-03-25 North West 21
## 590 2020-03-26 North West 29
## 591 2020-03-27 North West 36
## 592 2020-03-28 North West 28
## 593 2020-03-29 North West 46
## 594 2020-03-30 North West 67
## 595 2020-03-31 North West 52
## 596 2020-04-01 North West 86
## 597 2020-04-02 North West 96
## 598 2020-04-03 North West 95
## 599 2020-04-04 North West 98
## 600 2020-04-05 North West 102
## 601 2020-04-06 North West 100
## 602 2020-04-07 North West 135
## 603 2020-04-08 North West 127
## 604 2020-04-09 North West 119
## 605 2020-04-10 North West 117
## 606 2020-04-11 North West 138
## 607 2020-04-12 North West 125
## 608 2020-04-13 North West 129
## 609 2020-04-14 North West 131
## 610 2020-04-15 North West 114
## 611 2020-04-16 North West 135
## 612 2020-04-17 North West 98
## 613 2020-04-18 North West 113
## 614 2020-04-19 North West 71
## 615 2020-04-20 North West 83
## 616 2020-04-21 North West 76
## 617 2020-04-22 North West 86
## 618 2020-04-23 North West 85
## 619 2020-04-24 North West 66
## 620 2020-04-25 North West 66
## 621 2020-04-26 North West 55
## 622 2020-04-27 North West 54
## 623 2020-04-28 North West 57
## 624 2020-04-29 North West 63
## 625 2020-04-30 North West 59
## 626 2020-05-01 North West 45
## 627 2020-05-02 North West 56
## 628 2020-05-03 North West 55
## 629 2020-05-04 North West 48
## 630 2020-05-05 North West 48
## 631 2020-05-06 North West 44
## 632 2020-05-07 North West 49
## 633 2020-05-08 North West 42
## 634 2020-05-09 North West 31
## 635 2020-05-10 North West 42
## 636 2020-05-11 North West 35
## 637 2020-05-12 North West 38
## 638 2020-05-13 North West 25
## 639 2020-05-14 North West 26
## 640 2020-05-15 North West 33
## 641 2020-05-16 North West 32
## 642 2020-05-17 North West 24
## 643 2020-05-18 North West 31
## 644 2020-05-19 North West 35
## 645 2020-05-20 North West 27
## 646 2020-05-21 North West 27
## 647 2020-05-22 North West 26
## 648 2020-05-23 North West 31
## 649 2020-05-24 North West 26
## 650 2020-05-25 North West 31
## 651 2020-05-26 North West 27
## 652 2020-05-27 North West 27
## 653 2020-05-28 North West 28
## 654 2020-05-29 North West 20
## 655 2020-05-30 North West 19
## 656 2020-05-31 North West 13
## 657 2020-06-01 North West 12
## 658 2020-06-02 North West 27
## 659 2020-06-03 North West 22
## 660 2020-06-04 North West 22
## 661 2020-06-05 North West 16
## 662 2020-06-06 North West 26
## 663 2020-06-07 North West 20
## 664 2020-06-08 North West 23
## 665 2020-06-09 North West 17
## 666 2020-06-10 North West 16
## 667 2020-06-11 North West 16
## 668 2020-06-12 North West 11
## 669 2020-06-13 North West 10
## 670 2020-06-14 North West 15
## 671 2020-06-15 North West 16
## 672 2020-06-16 North West 15
## 673 2020-06-17 North West 13
## 674 2020-06-18 North West 13
## 675 2020-06-19 North West 7
## 676 2020-06-20 North West 11
## 677 2020-06-21 North West 7
## 678 2020-06-22 North West 11
## 679 2020-06-23 North West 13
## 680 2020-06-24 North West 13
## 681 2020-06-25 North West 15
## 682 2020-06-26 North West 6
## 683 2020-06-27 North West 7
## 684 2020-06-28 North West 9
## 685 2020-06-29 North West 8
## 686 2020-06-30 North West 6
## 687 2020-07-01 North West 3
## 688 2020-07-02 North West 6
## 689 2020-07-03 North West 6
## 690 2020-07-04 North West 4
## 691 2020-07-05 North West 6
## 692 2020-07-06 North West 9
## 693 2020-07-07 North West 7
## 694 2020-07-08 North West 5
## 695 2020-07-09 North West 10
## 696 2020-07-10 North West 2
## 697 2020-07-11 North West 3
## 698 2020-07-12 North West 0
## 699 2020-07-13 North West 6
## 700 2020-07-14 North West 2
## 701 2020-07-15 North West 4
## 702 2020-07-16 North West 2
## 703 2020-07-17 North West 1
## 704 2020-07-18 North West 2
## 705 2020-07-19 North West 0
## 706 2020-03-01 South East 0
## 707 2020-03-02 South East 0
## 708 2020-03-03 South East 1
## 709 2020-03-04 South East 0
## 710 2020-03-05 South East 1
## 711 2020-03-06 South East 0
## 712 2020-03-07 South East 0
## 713 2020-03-08 South East 1
## 714 2020-03-09 South East 1
## 715 2020-03-10 South East 1
## 716 2020-03-11 South East 1
## 717 2020-03-12 South East 0
## 718 2020-03-13 South East 1
## 719 2020-03-14 South East 1
## 720 2020-03-15 South East 5
## 721 2020-03-16 South East 8
## 722 2020-03-17 South East 7
## 723 2020-03-18 South East 10
## 724 2020-03-19 South East 9
## 725 2020-03-20 South East 13
## 726 2020-03-21 South East 7
## 727 2020-03-22 South East 25
## 728 2020-03-23 South East 20
## 729 2020-03-24 South East 22
## 730 2020-03-25 South East 29
## 731 2020-03-26 South East 35
## 732 2020-03-27 South East 34
## 733 2020-03-28 South East 36
## 734 2020-03-29 South East 55
## 735 2020-03-30 South East 58
## 736 2020-03-31 South East 65
## 737 2020-04-01 South East 66
## 738 2020-04-02 South East 55
## 739 2020-04-03 South East 72
## 740 2020-04-04 South East 80
## 741 2020-04-05 South East 82
## 742 2020-04-06 South East 88
## 743 2020-04-07 South East 100
## 744 2020-04-08 South East 83
## 745 2020-04-09 South East 104
## 746 2020-04-10 South East 88
## 747 2020-04-11 South East 88
## 748 2020-04-12 South East 88
## 749 2020-04-13 South East 84
## 750 2020-04-14 South East 65
## 751 2020-04-15 South East 72
## 752 2020-04-16 South East 56
## 753 2020-04-17 South East 86
## 754 2020-04-18 South East 57
## 755 2020-04-19 South East 70
## 756 2020-04-20 South East 87
## 757 2020-04-21 South East 51
## 758 2020-04-22 South East 54
## 759 2020-04-23 South East 57
## 760 2020-04-24 South East 64
## 761 2020-04-25 South East 51
## 762 2020-04-26 South East 51
## 763 2020-04-27 South East 41
## 764 2020-04-28 South East 40
## 765 2020-04-29 South East 47
## 766 2020-04-30 South East 29
## 767 2020-05-01 South East 37
## 768 2020-05-02 South East 36
## 769 2020-05-03 South East 17
## 770 2020-05-04 South East 35
## 771 2020-05-05 South East 29
## 772 2020-05-06 South East 25
## 773 2020-05-07 South East 27
## 774 2020-05-08 South East 26
## 775 2020-05-09 South East 28
## 776 2020-05-10 South East 19
## 777 2020-05-11 South East 25
## 778 2020-05-12 South East 27
## 779 2020-05-13 South East 18
## 780 2020-05-14 South East 32
## 781 2020-05-15 South East 25
## 782 2020-05-16 South East 22
## 783 2020-05-17 South East 18
## 784 2020-05-18 South East 22
## 785 2020-05-19 South East 12
## 786 2020-05-20 South East 22
## 787 2020-05-21 South East 15
## 788 2020-05-22 South East 17
## 789 2020-05-23 South East 21
## 790 2020-05-24 South East 17
## 791 2020-05-25 South East 13
## 792 2020-05-26 South East 19
## 793 2020-05-27 South East 18
## 794 2020-05-28 South East 12
## 795 2020-05-29 South East 21
## 796 2020-05-30 South East 8
## 797 2020-05-31 South East 12
## 798 2020-06-01 South East 11
## 799 2020-06-02 South East 13
## 800 2020-06-03 South East 18
## 801 2020-06-04 South East 11
## 802 2020-06-05 South East 11
## 803 2020-06-06 South East 10
## 804 2020-06-07 South East 12
## 805 2020-06-08 South East 8
## 806 2020-06-09 South East 10
## 807 2020-06-10 South East 11
## 808 2020-06-11 South East 5
## 809 2020-06-12 South East 6
## 810 2020-06-13 South East 7
## 811 2020-06-14 South East 7
## 812 2020-06-15 South East 8
## 813 2020-06-16 South East 13
## 814 2020-06-17 South East 9
## 815 2020-06-18 South East 4
## 816 2020-06-19 South East 7
## 817 2020-06-20 South East 5
## 818 2020-06-21 South East 3
## 819 2020-06-22 South East 2
## 820 2020-06-23 South East 8
## 821 2020-06-24 South East 7
## 822 2020-06-25 South East 5
## 823 2020-06-26 South East 8
## 824 2020-06-27 South East 8
## 825 2020-06-28 South East 6
## 826 2020-06-29 South East 5
## 827 2020-06-30 South East 5
## 828 2020-07-01 South East 2
## 829 2020-07-02 South East 7
## 830 2020-07-03 South East 3
## 831 2020-07-04 South East 5
## 832 2020-07-05 South East 4
## 833 2020-07-06 South East 3
## 834 2020-07-07 South East 5
## 835 2020-07-08 South East 3
## 836 2020-07-09 South East 7
## 837 2020-07-10 South East 3
## 838 2020-07-11 South East 1
## 839 2020-07-12 South East 3
## 840 2020-07-13 South East 4
## 841 2020-07-14 South East 3
## 842 2020-07-15 South East 3
## 843 2020-07-16 South East 3
## 844 2020-07-17 South East 1
## 845 2020-07-18 South East 4
## 846 2020-07-19 South East 0
## 847 2020-03-01 South West 0
## 848 2020-03-02 South West 0
## 849 2020-03-03 South West 0
## 850 2020-03-04 South West 0
## 851 2020-03-05 South West 0
## 852 2020-03-06 South West 0
## 853 2020-03-07 South West 0
## 854 2020-03-08 South West 0
## 855 2020-03-09 South West 0
## 856 2020-03-10 South West 0
## 857 2020-03-11 South West 1
## 858 2020-03-12 South West 0
## 859 2020-03-13 South West 0
## 860 2020-03-14 South West 1
## 861 2020-03-15 South West 0
## 862 2020-03-16 South West 0
## 863 2020-03-17 South West 2
## 864 2020-03-18 South West 2
## 865 2020-03-19 South West 4
## 866 2020-03-20 South West 3
## 867 2020-03-21 South West 6
## 868 2020-03-22 South West 7
## 869 2020-03-23 South West 8
## 870 2020-03-24 South West 7
## 871 2020-03-25 South West 9
## 872 2020-03-26 South West 11
## 873 2020-03-27 South West 13
## 874 2020-03-28 South West 21
## 875 2020-03-29 South West 18
## 876 2020-03-30 South West 23
## 877 2020-03-31 South West 23
## 878 2020-04-01 South West 21
## 879 2020-04-02 South West 23
## 880 2020-04-03 South West 30
## 881 2020-04-04 South West 42
## 882 2020-04-05 South West 32
## 883 2020-04-06 South West 34
## 884 2020-04-07 South West 39
## 885 2020-04-08 South West 47
## 886 2020-04-09 South West 24
## 887 2020-04-10 South West 46
## 888 2020-04-11 South West 43
## 889 2020-04-12 South West 23
## 890 2020-04-13 South West 27
## 891 2020-04-14 South West 24
## 892 2020-04-15 South West 32
## 893 2020-04-16 South West 29
## 894 2020-04-17 South West 33
## 895 2020-04-18 South West 25
## 896 2020-04-19 South West 31
## 897 2020-04-20 South West 26
## 898 2020-04-21 South West 26
## 899 2020-04-22 South West 23
## 900 2020-04-23 South West 17
## 901 2020-04-24 South West 19
## 902 2020-04-25 South West 15
## 903 2020-04-26 South West 27
## 904 2020-04-27 South West 13
## 905 2020-04-28 South West 17
## 906 2020-04-29 South West 15
## 907 2020-04-30 South West 26
## 908 2020-05-01 South West 6
## 909 2020-05-02 South West 7
## 910 2020-05-03 South West 10
## 911 2020-05-04 South West 17
## 912 2020-05-05 South West 14
## 913 2020-05-06 South West 19
## 914 2020-05-07 South West 16
## 915 2020-05-08 South West 6
## 916 2020-05-09 South West 11
## 917 2020-05-10 South West 5
## 918 2020-05-11 South West 8
## 919 2020-05-12 South West 7
## 920 2020-05-13 South West 7
## 921 2020-05-14 South West 6
## 922 2020-05-15 South West 4
## 923 2020-05-16 South West 4
## 924 2020-05-17 South West 6
## 925 2020-05-18 South West 4
## 926 2020-05-19 South West 6
## 927 2020-05-20 South West 1
## 928 2020-05-21 South West 9
## 929 2020-05-22 South West 6
## 930 2020-05-23 South West 6
## 931 2020-05-24 South West 3
## 932 2020-05-25 South West 8
## 933 2020-05-26 South West 11
## 934 2020-05-27 South West 5
## 935 2020-05-28 South West 10
## 936 2020-05-29 South West 7
## 937 2020-05-30 South West 3
## 938 2020-05-31 South West 2
## 939 2020-06-01 South West 7
## 940 2020-06-02 South West 2
## 941 2020-06-03 South West 7
## 942 2020-06-04 South West 2
## 943 2020-06-05 South West 2
## 944 2020-06-06 South West 1
## 945 2020-06-07 South West 3
## 946 2020-06-08 South West 3
## 947 2020-06-09 South West 0
## 948 2020-06-10 South West 1
## 949 2020-06-11 South West 2
## 950 2020-06-12 South West 2
## 951 2020-06-13 South West 2
## 952 2020-06-14 South West 0
## 953 2020-06-15 South West 2
## 954 2020-06-16 South West 2
## 955 2020-06-17 South West 0
## 956 2020-06-18 South West 0
## 957 2020-06-19 South West 0
## 958 2020-06-20 South West 2
## 959 2020-06-21 South West 0
## 960 2020-06-22 South West 1
## 961 2020-06-23 South West 1
## 962 2020-06-24 South West 1
## 963 2020-06-25 South West 0
## 964 2020-06-26 South West 3
## 965 2020-06-27 South West 0
## 966 2020-06-28 South West 0
## 967 2020-06-29 South West 1
## 968 2020-06-30 South West 0
## 969 2020-07-01 South West 0
## 970 2020-07-02 South West 0
## 971 2020-07-03 South West 0
## 972 2020-07-04 South West 0
## 973 2020-07-05 South West 1
## 974 2020-07-06 South West 0
## 975 2020-07-07 South West 0
## 976 2020-07-08 South West 2
## 977 2020-07-09 South West 0
## 978 2020-07-10 South West 1
## 979 2020-07-11 South West 0
## 980 2020-07-12 South West 0
## 981 2020-07-13 South West 1
## 982 2020-07-14 South West 0
## 983 2020-07-15 South West 0
## 984 2020-07-16 South West 0
## 985 2020-07-17 South West 1
## 986 2020-07-18 South West 0
## 987 2020-07-19 South West 01.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Monday 20 Jul 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -13.7184 -4.8961 -0.4672 3.8101 8.6431
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.557e+00 6.582e-02 69.24 <2e-16 ***
## note_lag 1.485e-05 6.862e-07 21.64 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 25.61314)
##
## Null deviance: 13033.9 on 79 degrees of freedom
## Residual deviance: 2114.1 on 78 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 95.336763 1.000015
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 83.621875 108.244969
## note_lag 1.000014 1.000016
Rsq(lag_mod)
## [1] 0.8378011
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.4.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.14
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.2.0
## [10] projections_0.5.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.1 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.5 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.0 tibble_3.0.3 ggplot2_3.3.2
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-148 fs_1.4.2 webshot_0.5.2 httr_1.4.2
## [5] rprojroot_1.3-2 tools_4.0.2 backports_1.1.8 utf8_1.1.4
## [9] R6_2.4.1 mgcv_1.8-31 DBI_1.1.0 colorspace_1.4-1
## [13] withr_2.2.0 gridExtra_2.3 tidyselect_1.1.0 sodium_1.1
## [17] curl_4.3 compiler_4.0.2 cli_2.0.2 labeling_0.3
## [21] matchmaker_0.1.1 scales_1.1.1 digest_0.6.25 foreign_0.8-80
## [25] rmarkdown_2.3 pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_1.4.4
## [29] htmlwidgets_1.5.1 rlang_0.4.7 readxl_1.3.1 rstudioapi_0.11
## [33] farver_2.0.3 generics_0.0.2 jsonlite_1.7.0 crosstalk_1.1.0.1
## [37] car_3.0-8 zip_2.0.4 magrittr_1.5 kyotil_2019.11-22
## [41] Matrix_1.2-18 Rcpp_1.0.5 munsell_0.5.0 fansi_0.4.1
## [45] viridis_0.5.1 abind_1.4-5 lifecycle_0.2.0 stringi_1.4.6
## [49] yaml_2.2.1 carData_3.0-4 snakecase_0.11.0 MASS_7.3-51.6
## [53] plyr_1.8.6 grid_4.0.2 blob_1.2.1 crayon_1.3.4
## [57] lattice_0.20-41 cowplot_1.0.0 splines_4.0.2 haven_2.3.1
## [61] hms_0.5.3 knitr_1.29 pillar_1.4.6 boot_1.3-25
## [65] ggsignif_0.6.0 reprex_0.3.0 glue_1.4.1 evaluate_0.14
## [69] data.table_1.12.8 modelr_0.1.8 vctrs_0.3.2 selectr_0.4-2
## [73] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1 xfun_0.15
## [77] openxlsx_4.1.5 broom_0.7.0 rstatix_0.6.0 survival_3.1-12
## [81] viridisLite_0.3.0 ellipsis_0.3.1